carbon_tower_streams %>% 
  mutate(date = as_date(date_time)) %>% 
  group_by(year(date), month(date)) %>% 
  tally() %>% 
  ungroup() %>% 
  rename("Year" = `year(date)`,
         "Month" = `month(date)`,
         "Total observations" = "n") %>% 
  gt() %>% 
  tab_header(
    title = md("**Total observations per date**"),
    subtitle = ("For the carbon tower data streams")
  ) 

# Evapotranspiration
carbon_tower_streams %>% 
  mutate(date = ymd_hms(date_time)) %>%
  ggplot(aes(x = date_time, y = evapotranspiration)) +
  geom_point() +
  # Si tenemos datetime, cambiamos el scale_x
  scale_x_datetime(date_labels = "%d-%b-%Y",  date_breaks = "1 day") +
  theme_light(base_size = 16) +
  theme(axis.text.x = element_text(angle = 90)) +
  labs(title = "Evapotranspiration from the Carbon tower in Santa Rosa",
       subtitle = "Data from the streams sensor station that includes the
       evapotranspiration calculation",
       x = "Date", y = "Evapotranspiration kg m-2 s-1")

# day <- interval(hms("06:00:00"), hms("17:30:00"))
# carbon_tower_streams %>%
#   mutate(day = ifelse(hms(date_time) > hms("06:00:00") & 
#                         hms(date_time) > hms("17:00:00"), 
#                       "dia", "noche")) %>%
#   select(date_time, day) %>%  View()

carbon_tower_streams %>% 
  separate(col = date_time, into = c("date", "time"), sep = " ") %>% 
  mutate(time = hms(time),
         day = ifelse(hms(time) > hms("05:20:00") & 
                        hms(time) < hms("17:20:00"),
                      "day", "nigth")) %>%
  replace_na(list(day = "nigth")) %>% 
  ggplot(aes(x = date, y = evapotranspiration, colour = day)) +
  geom_jitter(alpha = 0.5, size = 2) +
  theme_light(base_size = 12) +
  theme(axis.text.x = element_text(angle = 75, h = 1)) +
  scale_y_continuous(breaks = seq(-0.2, 0.2, by = 0.02)) +
  labs(title = "Evapotranspiration carbon tower Santa Rosa",
       subtitle = "Day defined as the interval of date time from 05:20:00 to 17:20:00",
       x = "Date", 
       y = "Evapotranspiration kg m-2 s-1",
       fill = "Day period")

# Check nigth values with evapotranspiration close to day values
## above 0.04 we can try to explore those values in the nigth category
carbon_tower_streams %>% 
  separate(col = date_time, into = c("date", "time"), sep = " ") %>% 
  mutate(time = hms(time),
         day = ifelse(hms(time) > hms("05:20:00") & 
                        hms(time) < hms("17:20:00"),
                      "day", "nigth")) %>%
  # This replace is due to the 00:00 time format
  replace_na(list(day = "nigth")) %>% 
  filter(evapotranspiration > 0.04) %>% 
  DT::datatable(
    rownames = FALSE,
    extensions = 'Buttons',
    selection = list(mode = 'single', target = 'row'),
    options = list(
      lengthMenu = list(c(20, 50, 100, -1), c('20', '50', '100', 'All')),
      dom = 'Bflp',
      buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
      scrollX = T)) 

# Revisión de datos de evapotranspiracion y latent heat
check <- carbon_tower_streams %>% 
  separate(col = date_time, into = c("date", "time"), sep = " ") %>% 
  mutate(time = hms(time),
         day = ifelse(hms(time) > hms("05:20:00") & 
                        hms(time) < hms("17:20:00"),
                      "day", "nigth")) %>%
  replace_na(list(day = "nigth")) %>% 
  mutate(eva_plus = ifelse(evapotranspiration > 0.04, "high", "low"))

# ¿Qué características tienen los puntos de noche con eva > 0.04?
check %>% 
  select(time, day, evapotranspiration, latent_heat_flux) %>% 
  filter(evapotranspiration > 0.04, day == "nigth") %>% 
  arrange(desc(evapotranspiration)) %>% 
  head(15) %>% 
  gt() %>% 
  tab_header(
    title = "High  evapotranspiration values during nigth",
    subtitle = "For the carbon tower data streams"
  )

# Notar que el latent heat flux es la multiplicacion de
# evapotranspiracion * mil

# check %>% 
#   # filter(eva_plus == "high") %>% 
#   ggplot(aes(x = evapotranspiration, y = latent_heat_flux, colour = day)) +
#   geom_jitter(alpha = 0.3, size = 2) +
#   theme_light()

# Water vapor mass density carbon
carbon_tower_streams %>% 
  mutate(date = ymd_hms(date_time)) %>%
  ggplot(aes(x = date_time, y = ambient_water_vapor_mass_density)) +
  geom_line() +
  # Si tenemos datetime, cambiamos el scale_x
  scale_x_datetime(date_labels = "%d-%b-%Y",  date_breaks = "1 day") +
  theme_light(base_size = 16) +
  theme(axis.text.x = element_text(angle = 90)) +
  labs(title = "Water vapor mass density carbon tower Santa Rosa",
       x = "Date", y = "Water vapor mass density kg m-3")

# Latent heat flux
carbon_tower_streams %>% 
  mutate(date = ymd_hms(date_time)) %>%
  ggplot(aes(x = date_time, y = latent_heat_flux)) +
  geom_line() +
  # Si tenemos datetime, cambiamos el scale_x
  scale_x_datetime(date_labels = "%d-%b-%Y",  date_breaks = "1 day") +
  theme_light(base_size = 16) +
  theme(axis.text.x = element_text(angle = 90)) +
  labs(title = "Latent heat flux carbon tower Santa Rosa",
       x = "Date", y = "Latent heat flux W m-2")

# Net ecosystem exchange
carbon_tower_streams %>% 
  mutate(date = ymd_hms(date_time)) %>%
  ggplot(aes(x = date_time, y = net_ecosystem_exchange_mmol_m_2_s_1 )) +
  geom_line() +
  # Si tenemos datetime, cambiamos el scale_x
  scale_x_datetime(date_labels = "%d-%b-%Y",  date_breaks = "1 day") +
  theme_light(base_size = 16) +
  theme(axis.text.x = element_text(angle = 90)) +
  labs(title = "Net ecosystem exchange carbon tower Santa Rosa",
       x = "Date", y = "Net ecosystem exchange mmol m2 s1")

carbon_tower_streams %>% 
  mutate(date = ymd_hms(date_time)) %>%
  ggplot(aes(y = evapotranspiration, 
             x = net_ecosystem_exchange_mmol_m_2_s_1 )) +
  geom_point() +
  geom_smooth(method = "lm") +
  theme_light(base_size = 16)

carbon_tower_streams %>% 
  mutate(date = ymd_hms(date_time)) %>%
  ggplot(aes(y = evapotranspiration, 
             x = latent_heat_flux)) +
  geom_point() +
  geom_smooth(method = "lm") +
  theme_light(base_size = 16)

carbon_tower_streams %>% 
  mutate(date = ymd_hms(date_time)) %>%
  ggplot(aes(y = evapotranspiration, 
             x = ambient_water_vapor_mass_density)) +
  geom_point() +
  geom_smooth(method = "lm") +
  theme_light(base_size = 16)

evapotranspiration <- eddy %>% 
  mutate(evapotranspiration_kg = LE.to.ET(latent_heat_flux,
                                          mean_thermocouple_temp)) %>% 
  # Transform units
  mutate(evapotranspiration_mol = kg.to.mol((evapotranspiration_kg) * 1000))

evapotranspiration %>% 
  select(latent_heat_flux, evapotranspiration_kg) %>% 
  head(20) %>% 
  gt() %>% 
  tab_header(
    title = md("**Comparison of Latent Heat Flux with Evapotranspiration**"),
    subtitle = "For the Carbon Tower eddy data"
  ) %>% 
  tab_footnote(
     footnote = "Shows only the first 20 observations",
     locations = cells_column_labels(
       columns = vars(latent_heat_flux, evapotranspiration_kg)
     )
  )

evapotranspiration %>% 
  mutate(date = ymd_hms(date_time)) %>%
  # filter values above 0.05
  filter(evapotranspiration_mol < 1000 & evapotranspiration_mol > -500) %>%
  # filter(year(date_time) == 2013) %>% 
  ggplot(aes(x = date_time, y = evapotranspiration_mol)) +
  geom_point(alpha = 0.5) +
  scale_x_datetime(date_labels = "%b", breaks = "months") +
  theme_light(base_size = 21) +
  theme(axis.text.x = element_text(angle = 75, h = 1)) +
  labs(title = "Evapotranspiration carbon tower Santa Rosa",
       subtitle = "Al the datapoints available",
       x = "Date", y = "Evapotranspiration mmol m-2 s-1")

plot_evapotranspiration <- function(data, year) {
  data %>%
    mutate(date = ymd_hms(date_time),
           year = year(date_time)) %>%
    # filter values above 0.05
    # filter(evapotranspiration < 0.02 & evapotranspiration > -0.02) %>%
    filter(year == !!year) %>%
    ggplot(aes(x = date_time, y = evapotranspiration_mol)) +
    geom_point(alpha = 0.5) +
    # geom_line() +
    scale_x_datetime(date_labels = "%b", breaks = "months") +
    theme_light(base_size = 16) +
    theme(axis.text.x = element_text(angle = 75, h = 1)) +
    labs(title = "Evapotranspiration carbon tower Santa Rosa",
         subtitle = paste("With all the datapoints available for", year),
         x = "Date", y = "Evapotranspiration mmol m-2 s-1")
}

## Get years
years <- evapotranspiration %>% 
  transmute(year = year(date_time)) %>% 
  distinct() %>% 
  pull()

# Map to get all plots
map(.x = years, .f = function(plots) {
  evapotranspiration %>% 
    plot_evapotranspiration(year = plots)
})

evapotranspiration <- eddy %>% 
  mutate(evapotranspiration_kg = LE.to.ET(latent_heat_flux,
                                       air_temperature)) %>% 
  # Transform units
  mutate(evapotranspiration_mol = kg.to.mol((evapotranspiration_kg) * 1000))

evapotranspiration %>% 
  select(latent_heat_flux, evapotranspiration_kg) %>% 
  head(20) %>% 
  gt() %>% 
  tab_header(
    title = md("**Comparison of Latent Heat Flux with Evapotranspiration**"),
    subtitle = "For the Principe tower eddy data"
  ) %>% 
  tab_footnote(
     footnote = "Shows only the first 20 observations",
     locations = cells_column_labels(
       columns = vars(latent_heat_flux, evapotranspiration_kg)
     )
  )

evapotranspiration %>% 
  mutate(date = ymd_hms(date_time)) %>%
  # filter values above 0.05
  # filter(evapotranspiration < 0.02 & evapotranspiration > -0.02) %>% 
  # filter(year(date_time) == 2013) %>% 
  ggplot(aes(x = date_time, y = evapotranspiration_mol)) +
  geom_point(alpha = 0.5) +
  scale_x_datetime(date_labels = "%b", breaks = "months") +
  theme_light(base_size = 16) +
  theme(axis.text.x = element_text(angle = 75, h = 1)) +
  labs(title = "Evapotranspiration Principe tower Santa Rosa",
       subtitle = "Al the datapoints available",
       x = "Date", y = "Evapotranspiration mmol m-2 s-1")

plot_evapotranspiration <- function(data, year) {
  data %>%
    mutate(date = ymd_hms(date_time),
           year = year(date_time)) %>%
    # filter values above 0.05
    # filter(evapotranspiration < 0.02 & evapotranspiration > -0.02) %>%
    filter(year == !!year) %>%
    ggplot(aes(x = date_time, y = evapotranspiration_mol)) +
    geom_point(alpha = 0.5) +
    # geom_line() +
    scale_x_datetime(date_labels = "%b", breaks = "months") +
    theme_light(base_size = 16) +
    theme(axis.text.x = element_text(angle = 75, h = 1)) +
    labs(title = "Evapotranspiration Principe tower Santa Rosa",
         subtitle = paste("With all the datapoints available for", year),
         x = "Date", y = "Evapotranspiration kg m-2 s-1")
}

## Get years
years <- evapotranspiration %>% 
  transmute(year = year(date_time)) %>% 
  distinct() %>% 
  pull()

# Map to get all plots
map(.x = years, .f = function(plots) {
  evapotranspiration %>% 
    plot_evapotranspiration(year = plots)
})

LE <- seq(300, 500, by = 50)
Tair <- 25

ETkg <- LE.to.ET(LE, Tair)
(ETmmol <- kg.to.mol(ETkg) * 1000)

evapotranspiration <- eddy %>% 
  mutate(evapotranspiration_kg = LE.to.ET(latent_heat_flux,
                                       air_temperature)) %>% 
  # Transform units
  mutate(evapotranspiration_mol = kg.to.mol((evapotranspiration_kg) * 1000))

evapotranspiration %>% 
  mutate(date = ymd_hms(date_time)) %>%
  # filter values above 0.05
  # filter(evapotranspiration < 0.02 & evapotranspiration > -0.02) %>% 
  # filter(year(date_time) == 2013) %>% 
  ggplot(aes(x = date_time, y = evapotranspiration_mol)) +
  geom_point(alpha = 0.5) +
  scale_x_datetime(date_labels = "%b", breaks = "months") +
  theme_light(base_size = 21) +
  theme(axis.text.x = element_text(angle = 75, h = 1)) +
  labs(title = "Evapotranspiration Principe tower Santa Rosa",
       subtitle = "Al the datapoints available",
       x = "Date", y = "Evapotranspiration mmol m-2 s-1")